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Record W4407977272 · doi:10.1016/j.jtct.2025.01.182

Significant Inter-Laboratory Variability in Measurable Residual Disease Multiparameter Flow Cytometry Testing Prior to Allogeneic Transplantation Impedes Outcome Prediction: A CIBMTR Analysis

2025· article· en· W4407977272 on OpenAlexaff
Jesse M. Tettero, Gege Gui, Filippo Milano, Nelli Benjanyan, Veronika Bachanová, Larisa Broglie, Christopher S. Hourigan, Firas El Chaer

Bibliographic record

VenueTransplantation and Cellular Therapy · 2025
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsInstitute of Cancer Research
Fundersnot available
KeywordsTransplantationResidualMedicineOutcome (game theory)Flow cytometryDiseaseOncologyInternal medicineImmunologyComputer scienceMathematics

Abstract

fetched live from OpenAlex

Background Measurable residual disease (MRD) detected before allogeneic hematopoietic cell transplantation (alloHCT) is associated with an increased risk of relapse and mortality in patients with acute myeloid leukemia (AML). However, MRD assessments using multiparametric flow cytometry (MFC) across the U.S. are inconsistent. Objectives This study evaluated inter-laboratory variability in MFC-MRD testing and its association with relapse-free survival (RFS) and overall survival (OS) in patients with AML in first complete remission (CR1) prior to alloHCT. Methods AML patients in CR1 with available pre-HCT MFC-MRD results were included. Multivariable analyses (MVA) adjusted for clinical factors were performed to assess the relationship between the pre-alloHCT MFC status and post-alloHCT outcomes. Results Among 2,544 patients (median age 58 years; range 18-81) with AML, 282 (11.1%) had detectable MRD by MFC, correlating with a significantly higher risk of post-transplant relapse (RFS at 3 years: 47% vs. 35%; MVA hazard ratio (HR): 1.42, 95% Confidence Interval (CI): 1.17-1.72, p<0.001) and lower OS at 3 years (56% vs. 47%; HR=1.27, 95% CI: 1.06-1.51, p=0.009). The median number of patients per center was 19.4 (range, 1-121). Twelve centers reported data on >50 patients with a median MFC-MRD positivity of 15.5% (range: 1.3%–27.8%, p=0.07). Comparison of the MRD-neg vs. MRD-pos data from the two largest centers with similar rates of MFC-MRD positivity revealed substantial differences in survival outcomes: the largest center (n=121) showed relapse risk of 26% vs 58% (p=0.006), RFS of 61% vs. 29% (p=0.001; Figure 1A) and OS of 69% vs. 36% (p=0.008; Figure 1B), while the second center (n=88) displayed no significant differences (relapse risk: 38% vs 29%, p=0.52; RFS: 45% vs. 63%, p=0.64; OS: 50% vs. 77%, p=0.25; Figure 1C-D). In MVA, both RFS (HR=2.86, 95% CI: 1.57-5.20, p<0.001) and OS (HR=2.36, 95% CI: 1.23-4.54, p=0.001) remained significantly influenced by MRD status at the largest center, with the type of conditioning regimen as the only other significant factor. At the second center, MRD status was not significantly associated with outcomes in MVA (RFS: HR=0.98, 95% CI: 0.39-2.47, p=0.96 and OS: HR=0.53, 95% CI: 0.19-1.47, p=0.22). At baseline, the patients from the two centers only differed significantly in mean age (mean 51 vs. 56 years, p=0.01). Other ten sites showed similar RFS and OS to the second center, with no significant differences by MRD status. Conclusion MRD by MFC in CR1 prior to alloHCT is associated with post-alloHCT outcomes in patients with AML. However, substantial variability in prognostic value exists across U.S. centers, likely due to differences in MFC assessment. Therefore, there is an urgent need to harmonize and standardize MFC testing methodologies. The forthcoming nationwide MEASURE study will provide prospective validation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.054
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.303
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations3
Published2025
Admission routes1
Has abstractno

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